tidyxl 0.1.9

Import Excel files into tidy format with cell-level data extraction

A Python package that imports Excel files (.xlsx, .xlsm) into a tidy format where each cell is represented as a single row with detailed metadata. This package is inspired by and closely mirrors the functionality of the R tidyxl package.

Installation

pip install tidyxl

Quick Start

from tidyxl import xlsx_cells, xlsx_sheet_names

# Get all sheet names
sheets = xlsx_sheet_names("data.xlsx")
print(f"Found sheets: {sheets}")

# Read all cells from all sheets
cells = xlsx_cells("data.xlsx")
print(f"Total cells: {len(cells)}")

# Read specific sheet only
employees = xlsx_cells("data.xlsx", sheets="Employees")

# Filter for cells with content
content_cells = cells[~cells['is_blank']]
print(content_cells[['sheet', 'address', 'data_type', 'character', 'numeric']].head())

Features

• Tidy Format: Each Excel cell becomes one row with comprehensive metadata
• Complete Cell Information: Extract values, formulas, formatting, comments, and more
• Multiple Worksheets: Process all sheets or specify particular ones
• Named Ranges: Extract and analyze Excel named ranges and formulas
• Data Validation: Discover data validation rules applied to cells
• Type Safety: Separate columns for different data types (numeric, character, logical, date, error)
• R tidyxl Compatible: Identical API and output structure to the R package

Core Functions

xlsx_cells()

Extract all cell data in tidy format:

from tidyxl import xlsx_cells

# Read all sheets
cells = xlsx_cells("file.xlsx")

# Read specific sheets
cells = xlsx_cells("file.xlsx", sheets=["Sheet1", "Sheet2"])
cells = xlsx_cells("file.xlsx", sheets="Data")

# Include/exclude blank cells
cells = xlsx_cells("file.xlsx", include_blank_cells=False)

xlsx_sheet_names()

List all worksheet names:

from tidyxl import xlsx_sheet_names

sheets = xlsx_sheet_names("file.xlsx")
# Returns: ['Sheet1', 'Data', 'Summary']

xlsx_names()

Extract named ranges and formulas:

from tidyxl import xlsx_names

names = xlsx_names("file.xlsx")
print(names[['name', 'formula', 'sheet', 'is_range']])

xlsx_validation()

Extract data validation rules:

from tidyxl import xlsx_validation

validation = xlsx_validation("file.xlsx")
print(validation[['sheet', 'ref', 'type', 'formula1']])

xlsx_formats()

Extract formatting information:

from tidyxl import xlsx_formats

formats = xlsx_formats("file.xlsx")
# Returns dict with keys: fonts, fills, borders, number_formats

Output Structure

The xlsx_cells() function returns a pandas DataFrame with 23 columns matching the R tidyxl package:

Column	Type	Description
sheet	str	Worksheet name
address	str	Cell address (A1 notation)
row	int	Row number
col	int	Column number
is_blank	bool	Whether cell has a value
content	str	Raw cell value before type conversion
data_type	str	Cell type (character, numeric, logical, date, error, blank)
error	str	Error value if cell contains error
logical	bool	Boolean value if cell contains TRUE/FALSE
numeric	float	Numeric value if cell contains number
date	datetime	Date value if cell contains date
character	str	String value if cell contains text
formula	str	Formula if cell contains formula
is_array	bool	Whether formula is array formula
formula_ref	str	Range address for array/shared formulas
formula_group	int	Formula group identifier
comment	str	Cell comment text
height	float	Row height in Excel units
width	float	Column width in Excel units
row_outline_level	int	Row outline/grouping level
col_outline_level	int	Column outline/grouping level
style_format	str	Style format identifier
local_format_id	int	Local formatting identifier

License

This project is licensed under the MIT License.

Acknowledgments

• Inspired by the excellent R tidyxl package by Duncan Garmonsway
• Built on top of openpyxl for Excel file processing
• Uses pandas for data structure management